Biology subjects

Seidel, P.

Publications and source records attributed to Seidel, P..

5 recordsLinked to original sources

bioRxiv2025

Smart Microscopy: Current Implementations and a Roadmap for Interoperability

Smart microscopy is transforming life sciences by automating experimental imaging workflows and enabling real-time adaptation based on feedback from images and other data streams. This shift increases throughput, improves reproducibility, and expands the functional capabilities of microscopes. However, the current landscape is highly fragmented. Academic researchers often develop custom solutions for specific scientific needs, while industry offerings are typically proprietary and tied to specific hardware. This diversity, while fostering innovation, also creates major challenges in interoperability, reproducibility, and standardization, which slows progress and adaption. This article presents a collaborative effort between academic and industry leaders to survey the current state of smart microscopy, highlight representative implementations, and identify common technical and organizational barriers. We propose a framework for greater interoperability based on shared standards, modular software design, and community-driven development. Our goal is to support collaboration across the field and lay the groundwork for a more connected, reusable, and accessible smart microscopy ecosystem. We conclude with a call to action for researchers, hardware developers, and institutions to join in building an open, interoperable foundation that will unlock the full potential of smart microscopy in life science research.

cell biology↗

bioRxiv2025

Predicting Developmental Norms from Baseline Cortical Thickness in Longitudinal Studies

BackgroundNormative models have gained popularity in computational psychiatry for studying individual-level differences relative to population norms in biological data such as brain imaging, where measures like cortical thickness are typically predicted from variables such as age and sex. Nearly all published models to date are based on cross-sectional data, limiting their ability to predict longitudinal change. MethodsHere, we used longitudinal brain data from the Adolescent Brain Cognitive Development (ABCD) study, comprising cortical thickness measures from 180 regions per hemisphere in youths at baseline (N=6179; 47% females), 2-year (N=6179; 47% females), and 4-year (N=805; 45% females) follow-up. A training set was established from baseline and 2-year follow-up data (N=5374; 47% females), while data from individuals with all three time points available served as an independent test set (N=805; 45% females). We developed sex-specific Baseline-Conditioned Norms (B-Norms) that predict brain region thickness at follow-up based on baseline thickness, baseline age, and follow-up age, and compared them to sex-specific Cross-Sectional Norms (C-Norms) based on age alone. ResultsOut-of-sample testing in 2-year and 4-year follow-up data showed that B-Norms consistently provided better fits than C-Norms for nearly all cortical regions. Explained variance was higher in B-Norms than in C-Norms. No significant differences between time points (p = 0.45) were detected. Repeated measures ANOVA revealed differences in higher-order moments (e.g., skewness and kurtosis) for both models; for example, skewness varied by model, sex, time point, and their interactions. ConclusionWhile improved fit alone does not necessarily indicate a superior normative model, since normative models aim to capture population variance rather than simply optimize fit, we demonstrated that four regions were associated with pubertal changes in B-Norms but not in C-Norms, suggesting enhanced sensitivity of B-Norms to developmental processes. Together, our findings highlight the potential of B-Norms for capturing normative variation in longitudinal structural brain change. HighlightsO_LIBaseline-Conditioned (B-Norm) models consistently outperform Cross-Sectional (C-Norm) models across all cortical regions. C_LIO_LIB-Norm deviation scores show stronger associations with pubertal progression in females than C-Norm deviations, indicating higher developmental sensitivity. C_LIO_LIThis contrasts with brain-age models, where improved fit typically reduces the association between residuals and downstream clinical or developmental variation. C_LI SummaryNormative modeling has been applied to study how brain measures, such as gray matter thickness or volume, change across development. These models help identify how an individuals brain may differ from what is typical for their age or sex, which could eventually support more personalized treatments. However, most existing models use only one-time (cross-sectional) data, meaning they cannot capture how the brain changes over time. Longitudinal data, tracking the same individuals across multiple time points, is more informative but harder and more expensive to collect. We analyzed brain scans from over 6000 young people in the Adolescent Brain Cognitive Development (ABCD) study, about half of whom were girls. Each participant had brain scans at the start of the study, two and four years later. We showed that Baseline-Conditioned Norms (B-Norms), used each persons first scan and their ages at baseline and follow-up timepoint to predict later brain changes. We compared this to Cross-Sectional Norms (C-Norm), which only used age. B-Norms predicted brain thickness more accurately and importantly were better at detecting brain differences linked to puberty, especially in girls. Although better fit alone does not prove superiority, our findings suggest that using our proposed B-Norms, we potentially also capture more developmental variance suggesting that our B-Norms are possibly more sensitive to sex-specific brain development over time.

neuroscience↗

bioRxiv2025

Subliminal risk influences subjective value in the ventromedial prefrontal cortex

The relationship between conscious awareness and decisions has been heavily debated. Here we investigated whether subliminal probabilities are integrated with conscious rewards to form subjective value (SV) representations in the anterior ventral striatum (aVS) and ventromedial prefrontal cortex (vmPFC). Participants played an incentivized competitive game with risky choice to accumulate points across trials in a behavioral and fMRI experiment. The game was a modified attentional-blink paradigm that rendered a probability cue unseen (indicating a 100% or 0% chance to win a risky reward). Following the probability cue, participants chose between a safe (1 point with certainty) or risky option (>1 or 0 points depending on probability cue). The risky reward was either 2 or 5 points, varying across trials. In some trials the probability cue was absent (replaced by a random distractor) and the probability to win the risky reward was 50%. When probability cues were unseen, they did not influence choice, as value-maximizing choice (d) was not greater than chance, but they did influence reaction time in both experiments. Consistent with SV integration, the BOLD signal in aVS and vmPFC was higher for both conscious rewards (high > low) and subliminal probabilities (high > low) and could not be explained by subliminal salience (cue present > absent). Moreover, multivariate pattern similarity between conscious rewards and subliminal probabilities in vmPFC suggest integration into an abstract value representation. Additionally, we found brain-wide subliminal probability and salience effects. Taken together, these results suggest that conscious awareness is not necessary for probability to be integrated with conscious rewards to form an abstract "common currency" SV representation in vmPFC. Additionally, brain-wide subliminal probability and salience effects suggests information can have "global access" without conscious awareness.

neuroscience↗

bioRxiv2024

Neural and behavioral similarity-driven tuning curves for manipulable objects

In our daily activities, we encounter numerous objects that we successfully distinguish and recognize within a fraction of a second. This holds for coarse distinctions (e.g., cat vs. hammer) but also for more challenging within-category distinctions that require fine-grain analysis (e.g., cat vs. dog). The efficiency of this recognition depends on how the brain organizes object-related information. While several attempts have focused on unravelling large-scale organization principles, research on within-category organization of knowledge is rather limited. Here, we explored the fine-grain organization of object knowledge and investigated whether manipulable objects are organized and represented in terms of their similarity. To accomplish this, different groups of individuals participated in a behavioral and fMRI release from adaptation experiment. Adaptation was induced by presenting different exemplars of a particular object, and release from adaptation was elicited by the presentation of a deviant object. The relationship between adaptation and deviant objects was manipulated into four levels of similarity, measured by feature overlap between these objects. Our findings revealed that increasing object similarity provoked progressively slower reaction times and progressively weaker fMRI release from adaptation. Specifically, we identified similarity-driven tuning curves for the release from adaptation in the medial fusiform, collateral sulcus, parahippocampal gyri, lingual gyri, lateral occipital complex, and occipito-parietal cortex. These results suggest that the processing and representation of objects in the brain and our ability to perform fine discriminations between objects reflect real-world object similarity in a relatively parametric manner.

neuroscience↗

bioRxiv2019

Temporal signal-to-noise changes in combined multiband- and slice-accelerated echo-planar imaging with a 20- and 64-channel coil

Echo planar imaging (EPI) is the most common method of functional magnetic resonance imaging for acquiring the blood oxygenation level-dependent (BOLD) contrast. One of the primary benefits of using EPI is that an entire volume of the brain can be acquired on the order of two seconds. However, this speed benefit comes with a cost. Because imaging protocols are limited by hardware (e.g., fast gradient switching), researchers are forced to compromise between spatial resolution, temporal resolution, or whole-brain coverage. Earlier attempts to circumvent this problem included developing protocols in which slices of a volume were acquired faster (i.e., slice (S) acceleration), while more recent protocols allow for multiple slices to be acquired simultaneously (i.e., multiband (MB) acceleration). However, applying such acceleration methods can lead to a reduction in the temporal signal-to-noise ratio (tSNR), which is a critical measure of the stability of the signal over time. Here we show, in five healthy subjects, using a 20- and 64-channel receiver coil, that enabling S-acceleration consistently yielded, as expected, a substantial decrease in tSNR, regardless of the receiver coil employed, whereas tSNR decrease resulting from MB acceleration was less pronounced. Specifically, with the 20-channel coil, tSNR of upto 4-fold MB-acceleration is comparable to that of no acceleration, while up to 6-fold MB-acceleration with the 64-channel coil yields comparable tSNR to that of no acceleration. Moreover, observed tSNR losses tended to be localized to temporal, insular, and medial brain regions and were more noticeable in the 20-than in the 64-channel coil. Conversely, with the 64-channel coil, the tSNR in lateral frontoparietal regions remained relatively stable with increasing MB factors. Such methodological explorations can inform researchers and clinicians as to how they can optimize imaging protocols depending on the available hardware and the brain regions they want to investigate.

neuroscience↗